Why does it pay to be selfish in a MANET?

Routing protocols for a mobile ad hoc network have assumed that all mobile nodes voluntarily participate in forwarding others' packets. This was a reasonable assumption because all MNs in a MANET belonged to a single authority. In the near future, however, a MANET may consist of MNs that belong to many different organizations since numerous civilian applications are expected to crop up. In this situation, some MNs may run independently and purposely decide not to forward packets so as to save their own energy. This could potentially lead to network partitioning and corresponding performance degradation. To minimize such situations in MANETs, many studies have explored the use of both the carrot and the stick approaches by having reputation-based, credit-payment, and game theory schemes. This article summarizes existing schemes, identifies their relative advantages, and projects future directions     

Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.     

Deep Q-network-based auto scaling for service in a multi-access edge computing environment

In 5G networks, it is necessary to provide services while meeting various service requirements, such as high data rates and low latency, in response to dynamic network conditions. Multi-access edge computing (MEC) is a promising concept to meet these requirements. The MEC environment enables service providers to deploy their low latency services that are composed of multiple components. However, operating a service manually and attempting to satisfy the quality of service (QoS) requirements is difficult because many factors need to be considered in an MEC scenario. In this paper, we propose an auto-scaling method using deep Q-networks (DQN), which is a reinforcement learning algorithm, to resize the number of instances assigned to service. In our evaluation, compared to other baseline methods, the proposed approach maintains the appropriate number of instances effectively in response to dynamic traffic change while satisfying QoS and minimizing the cost of operating the service in the MEC environment. The proposed method was implemented as a module running in OpenStack and published as open-source software.     

In Vivo Photoacoustic Imaging of Livers Using Biodegradable Hyaluronic Acid‐Conjugated Silica Nanoparticles

The diagnosis of liver diseases is generally carried out via ultrasound imaging, computed tomography, and magnetic resonance imaging. The emerging photoacoustic imaging is an attractive alternative to diagnose even early stage of liver diseases providing high‐resolution anatomical and functional information in deep tissue noninvasively. However, the liver has insufficient photoacoustic contrast due to low optical absorbance in the near‐infrared windows. Here, a new hyaluronate–silica nanoparticle (HA–SiNP) conjugate for liver‐specific delivery and imaging for the diagnosis of liver diseases is developed. The HA–SiNP conjugates show high liver‐specific targeting efficiency, strong optical absorbance near‐infrared windows, excellent biocompatibility, and biodegradability. The liver‐specific targeting efficiency is verified by in vitro cellular uptake test, and in vivo and ex vivo photoacoustic imaging. In vivo photoacoustic imaging shows that photoacoustic amplitude in the liver injected with HA–SiNP conjugates is 4.4 times higher than that of the liver injected with SiNP. The biocompatibility and biodegradability of HA–SiNP conjugates are verified by cell viability test, optical spectrum analysis of urine, and inductively coupled plasma‐mass spectroscopy (ICP‐MS) analysis. Taken together, HA–SiNP conjugates may be developed as a promising liver targeted photoacoustic imaging contrast agent and liver‐targeted drug delivery agent.     

Protocol misbehavior detection framework using machine learning classification in vehicular Ad Hoc networks

Wireless Networks - A novel approach is proposed to detect protocol misbehavior using state-of-the-art machine learning frameworks and entropy. Nodes in Vehicular Ad Hoc Networks (VANETs) use...     

Thermosonic bonding of lead-free solder with metal bump for flip-chip bonding

While extensive research on the lead-free solder has been conducted, the high melting temperature of the lead-free solder has detrimental effects on the packages. Thermosonic bonding between metal bumps and lead-free solder using the longitudinal ultrasonic is investigated through numerical analysis and experiments for low-temperature soldering. The results of numerical calculation and measured viscoelastic properties show that a substantial amount of heat is generated in the solder bump due to viscoelastic heating. When the Au bump is thermosonically bonded to the lead-free solder bump (Sn-3%Ag-0.5%Cu), the entire Au bump is dissolved rapidly into the solder within 1 sec, which is caused by the scrubbing action of the ultrasonic. More reliable solder joints are obtained using the Cu/Ni/Au bump, which can be applied to flip-chip bonding.     

Adaptive mode decision for H.264 encoder

An adaptive mode decision algorithm is presented, with rate-distortion optimisation that reduces the complexity of the H.264 encoder without loss of image quality and compression ratio. The proposed algorithm uses the property of an all-zero coefficients block that is produced by quantisation and coefficient thresholding to effectively skip unnecessary modes. Experimental results show that the speed of the adaptive mode decision algorithm is two times faster than the full-mode decision algorithm of the JM72 reference encoder, without any coding loss.     

Nonvolatile flash memory device using Ge nanocrystals embedded in HfAlO high-/spl kappa/tunneling and control oxides: Device fabrication and electrical performance

We fabricated a nonvolatile Flash memory device using Ge nanocrystals (NCs) floating-gate (FG)-embedded in HfAlO high-/spl kappa/ tunneling/control oxides. Process compatibility and memory operation of the device were investigated. Results show that Ge-NC have good thermal stability in the HfAlO matrix as indicated by the negative Gibbs free energy changes for both reactions of GeO/sub 2/+Hf/spl rarr/HfO/sub 2/+Ge and 3GeO/sub 2/+4Al/spl rarr/2Al/sub 2/O/sub 3/+3Ge. This stability implies that the fabricated structure can be compatible with the standard CMOS process with the ability to sustain source-drain activation anneal temperatures. Compared with Si-NC embedded in HfO/sub 2/, Ge-NC embedded in HfAlO can provide more electron traps, thereby enlarging the memory window. It is also shown that this structure can achieve a low programming voltage of 6-7 V for fast programming, a long charge retention time of ten years maintaining a 0.7-V memory window, and good endurance characteristics of up to 10/sup 6/ rewrite cycles. This paper shows that the Ge-NC embedded in HfAlO is a promising candidate for further scaling of FG Flash memory devices.     

Three-dimensional localization of cochlear implant electrodes using epipolar stereophotogrammetry

Three-dimensional (3-D) localization of individual cochlear implant electrodes within the inner ear is of importance for modeling the electrical field of the cochlea, designing the electrode array, and programming the associated speech processor. A 3-D reconstruction method of cochlear implant electrodes is proposed to localize individual electrodes from two X-ray views in combination with the spiral computed tomography technique. By adapting epipolar geometry to the configuration of an X-ray imaging system, we estimate individual electrode locations in the least square sense without using a patient attachment required by an existing stereophotogrammetry technique. Furthermore, our method does not require any knowledge of the intrinsic and extrinsic parameters of the imaging system. The performance of our method is studied in numerical simulation and with patient data and is found to be sufficiently accurate for clinical use. The maximum root mean-square errors measured are 0.0445 and 0.214 mm for numerical simulation and patient data, respectively.